1. Field of the Invention
The present invention relates to a method for designing a coupled knowledge-base/database using a synthesized object-oriented entity-relationship model and, more particularly to a knowledge-based system and method for the retrieval of images using a coupled knowledge-base/database designed using a synthesized object-oriented entity-relationship model.
2. Discussion of the Background
Over the past two decades, databases have evolved into the central component of organizational information systems. A great many information systems are operated in environments requiring that a large amount of information or data be managed and processed. Further, the users of such systems often require high performance operation in information retrieval and processing functions.
Database and knowledge-base technologies have been widely employed by organizations to meet their information needs. Knowledge processing is widely accepted in business, industry, and engineering as valuable for handling information and has made possible advances in the capabilities of data processing and information systems.
Typically, a database maintains well-structured data representing the facts that traditionally are essential to data sharing and processing activities, while a knowledge-base contains less precise, more abstract, and possible subjective knowledge used mainly for decision and planning support. Because data come in larger quantities and are more dynamic than knowledge, database technologies are most often concerned with efficient update and retrieval operations on large sets of data whereas knowledge-base technologies address small-size knowledge-base processing and infrequent knowledge-base maintenance. Knowledge-based technologies have been applied to database systems to enhance data retrieval functions by providing decision and planning support.
One such effort to provide knowledge-based capabilities in a large, complex information system has been made in the area of image retrieval, and more particularly in a system for the retrieval of radiographic images for use in a hospital environment.
Advances in computer, communication and digital radiographic imaging technologies have encouraged many organizations to launch efforts to realize totally digitized radiology services. Extensive research and development is taking place in the areas of picture archiving and communication systems (PACS) that handle the creation, storage, retrieval, transmission and display of digital patient radiographic images and pertinent information for radiology-related services.
One of the core components of a PACS is a database system that stores and manages images and pertinent textual data. Massive amounts of data are generated every day (e.g., 3.8 gigabits in a 500 bed hospital) as new images in digital form are stored. In operation, the digitized images are retrieved from the PACS database for use by radiologists at viewing workstations. In order to meet the performance requirements of radiologists, a response time of less than two seconds is required. In an effort to satisfactorily handle the large amount of information and meet the performance goals required, some designs for PACS database systems have adopted a multi-level storage architecture and a distributed database approach.
Critical to the design of the PACS database system is the observation that in a radiological examination reading, radiologists usually compare a newly generated examination (image) with previous examinations (images) of the same patient. Based on this observation, the retrieval of old images is a critical design requirement of PACS.
In current film-based radiology systems, such retrieval is initially performed by nurses or assistants, who hang the most recent images on alternators for reading. During reading, radiologists may dig into film jackets and fan through other old images for additional relevant images. To effectively support digital primary reading using a PACS, it is essential to identify relevant previous patient images that can be either pre-fetched off-line or retrieved on-line to arrive for current diagnosis and to reduce the significant delay caused when access is obtained through a slow and remote storage device in a hierarchical (multi-level) and distributed PACS database system.
Effective patient image retrieval depends upon the radiologists' expert knowledge, which enables them to select images for comparison based on information about the new images (to be diagnosed), the patient and previous images. The implementation of the PACS system using a knowledge-based approach based on the expert knowledge of radiologists would provide the advantages of (1) reducing system response time, (2) reducing radiologists' time in selecting images for review, (3) minimizing the turnaround time of the exam interpretation function, and (4) improving the diagnostic effectiveness and quality by providing relevant and sufficient images automatically.
A precursor to the present invention is described in Sheng, Ovitt, Wang, and Garcia, Image Retrieval Expert Systems, Proc. SCAR 90 (Computer Applications to Assist Radiology), edited by Arenson and Friedenberg, pp. 198-204, and Sheng, Wang, and Garcia, IRES--Image Retrieval Expert System, Proc. SPIE Medical Imaging IV Conference, Feb. 4-9, 1990, pp. 832-841. The knowledge-based system for the retrieval of images, Image Retrieval Expert System (IRES), described was prototyped using three major components: databases, a procedural control algorithm, and a rule base. Although the capability of knowledge-based processing was added to the PACS, the design of IRES using a flat rule base resulted in problems with maintainability and extensibility of the system due to the lack of defined relationships among rules and the redundancy inherent in rule based systems. Further, the lack of a structured rule organization in the IRES rule base according to natural rule characterization and relationships negatively impacted upon the efficient design of knowledge inferencing procedures and severely hampered the extendibility of the knowledge-base to include a larger set of rules.
It is a known fact that as the structures and manipulations of databases become more complex and the size of knowledge-bases increase, as, for example, in the rule based IRES discussed above, existing separate database and knowledge-base design technologies become inadequate. In an effort to provide information systems having large, complex databases with knowledge processing capabilities, attempts have been made to couple knowledge-base and database design. Coupling knowledge-base and database design provides the advantages of (1) improving data management by using knowledge-base technologies to manage complex relationships among data and to perform deductive data processing, and (2) improving knowledge management by using database techniques to maintain the factual data imbedded in knowledge, thereby reducing the size and improving the extensibility and maintainability of knowledge-bases.
Modeling or representation of data and knowledge relationships is critical to the design of coupled knowledge-base/database systems. Modeling any less than all of the knowledge on data and knowledge interactions for a given application domain (1) severely restricts the maintainability and extensibility of the system, (2) limits the advantages realized by knowledge-base/database coupling, (3) greatly increases the burden on system designers/developers during the design and development stages, and (4) substantially increases the likelihood of errors in implementation.
Attempts to model data and knowledge relationships in designing coupled knowledge-base/database systems are characterized, generally, by the application of (1) semantic data modeling techniques or (2) object-oriented techniques to represent the knowledge/data relationships.
Semantic data modeling uses semantic models for representing structurally complex interrelationships among data. The primary components of semantic models are the explicit representation of objects, attributes of and relationships among objects, type constructors for building complex types, IS-A relationships, and derived schema components. An example of a semantic data model is the Entity-Relationship (ER) model. The ER model and other semantic data models and modeling techniques of the above-described type are discussed in Hull and King, Semantic Database Modeling: Survey, Applications, and Research Issues, ACM Computing Surveys, Vol. 19, No. 3, pp. 201-60, September 1987; and Peckham and Maryanski, Semantic Data Models, ACM Computing Surveys, Vol. 20, No. 3, pp. 153-89, September 1988. Although semantic data modeling provides a technique for effectively modeling the structural aspects of objects, no construct or mechanism is provided for representing the behavioral aspects of objects exhibited during knowledge and data interactions. This inherent limitation in the semantic data model precludes modeling of a substantial part of the knowledge exhibited during data and knowledge interactions. Further, the available semantic data modeling techniques are limited in the types of relationships among objects that can be represented and provide no mechanism for sharing of properties and methods among objects by inheritance.
Object-oriented modeling techniques have been applied to database designs to represent data relationships and their behavior. The Object-Oriented Entity-Relationship Model (OOERM), discussed in Gorman and Choobineh, An Overview of the Object-Oriented Entity Relationship Model (OOERM), Proceedings of the Twenty-Third Annual Hawaii International Conference on System Sciences, 1990, pp. 336-345, extended the Entity-Relationship (ER) model by using object-oriented constructs to model the operational properties of entities or objects for the purpose of database design. Because the ER model has been frequently used in database design methods, its object-oriented extension in the OOERM permits application of existing ER-based design concepts, while adding object-oriented principles to dictate entity behavior (procedures, rules or operation). The Object Modeling Technique (OMT), discussed in Blaha, Premerlani, and Rumbaugh, Relational Database Design Using An Object-Oriented Methodology, Communications of ACM, Vol. 31, No. 4, pp. 414-27, April 1988, similarly incorporated the main concepts of the ER model into an object-oriented model and associated design methods that model both the static (passive) and behavioral (active) properties of entities for software system and relational database designs. The application of object-oriented modeling techniques for database design provides the advantage of natural abstraction representation, data/behavior encapsulation and superclass-subclass inheritance features. However, these techniques (OOERM and OMT) cannot be effectively used to model coupled knowledge-base/database systems due to the limited types of entity or object behavior represented. This limitation of the OOERM and OMT precludes modeling of a crucial part of the knowledge exhibited during data and knowledge interactions.
Another object-oriented design method, the Structured Object Model (SOM), discussed in Higa, Morrision, Morrison, and Sheng, An Object-Oriented Methodology for Knowledge Base/Database Coupling, Working Paper, University of Arizona, 1990, and Morrison, Morrison, and Sheng, A Hierarchical Object-Oriented Knowledge-Based Architecture for Coupled Knowledge-Base/Database Systems, University of Arizona, Working Paper Series, 1990, has been used for modeling coupled knowledge-base/database systems. In the SOM design method, data semantics are represented using objects, attributes, and two types of relationships (aspect and specialization). Although this model provides the advantages associated with the object-oriented modeling method, the modeling constructs and the design procedures for the knowledge-base components are incomplete and imprecise, and cannot effectively represent the domain expert knowledge and data and knowledge relationships. Further, the problem solving control knowledge for performing the reasoning process in an object-oriented coupled knowledge-base/database system is not defined and no method for modeling such knowledge is described, as the systems were implemented using expert systems shells coupled with database management systems (DBMS).
Notwithstanding the available coupled knowledge-base/database design methods, there is a need for a method of designing a coupled knowledge-base/database system that provides (1) a mechanism for modeling all of the knowledge on data and knowledge interactions for a given application domain, (2) a defined construct for modeling all the knowledge, (3) a mechanism for sharing of properties and methods among objects by inheritance, and (4) a schema for the coupled database. Further, there is a need for a knowledge-based system and method for the retrieval of images that uses a coupled knowledge-base/database system having a knowledge-base storing all of the knowledge on data and knowledge interactions for the image retrieval process and a coupled database having a schema derived from the knowledge-base.